(The below text is licensed with CC0, which means that if you want to use or translate it, that is OK by me.)
Ok, I geeked out, and this is probably more information than you need. But it completely answers the question. Sorry. ☺
Locally, I'm at this commit:
$ git show
commit d6cd1e2bd19e03a81132a23b2025920577f84e37
Author: jnthn <jnthn@jnthn.net>
Date: Sun Apr 15 16:35:03 2012 +0200
NOTICE: I've summarized the perspectives of Octane's and Solid's creators here, for anyone short on time: https://gist.github.com/aleclarson/829c10aa7d287944ef75e1c2a90bef06
I'm a React/Preact/Astro developer with an interest in other renderers. The two that have most caught my eye recently are Octane (from Dominic Gannaway of React Hooks / Svelte / Inferno fame) and Solid 2.
I asked ChatGPT to compare them with its own "Renderer DX Pillars", which I've shared below. This is its gut instinct, with very little hedging:
| Pillar | Winner | Why |
|---|
| from qobuz_dl.bundle import Bundle | |
| bundle = Bundle() | |
| app_id = bundle.get_app_id() | |
| secrets = "\n".join(bundle.get_secrets().values()) | |
| print(f"App ID: {app_id}") | |
| print("#" * 20) |
| mode | ask |
|---|---|
| description | Document a Python file in the project, following specific conventions for style, formatting, and documentation. |
Please document the file ${file} following the conventions observed in the project.
CRITICAL: Code Preservation Policy
Unfortunately, I have found no such guide when I attempted this, so I am writing this down for posterity and so no one has to reinvent the wheel.
This method is ideal for me since I have 3D experience but absolutely suck at a 2D workflow, yet I need 2D assets for a 2D game.
Tested with Blender 4.4.0.
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.